Bathymetric Particle Filter SLAM Based on Mean Trajectory Map Representation

Zhang, Qianyi; Li, Ye; Zheng, Cong; Zhang, Wenjun

doi:10.1109/access.2021.3078854

Cited by 10 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The latter particle-filtering method based on Bayesian estimation shows strong performance in dealing with non-linear problems, requires low magnetic field measurement accuracy, allows for measurement errors, and has strong robustness. Zhang et al used particle filtering to achieve underwater terrain matching navigation [26]. In practical applications, as long as the filter does not diverge, the geomagnetic positioning error can always be maintained within a certain range.…”

Section: Principle Of Geomagnetic Matching Localizationmentioning

confidence: 99%

Underwater Geomagnetic Localization Based on Adaptive Fission Particle-Matching Technology

Yu,

Li,

Yang

et al. 2023

JMSE

View full text Add to dashboard Cite

The geomagnetic field constitutes a massive fingerprint database, and its unique structure provides potential position correction information. In recent years, particle filter technology has received more attention in the context of robot navigation. However, particle degradation and impoverishment have constrained navigation systems’ performance. This paper transforms particle filtering into a particle-matching positioning problem and proposes a geomagnetic localization method based on an adaptive fission particle filter. This method employs particle-filtering technology to construct a geomagnetic matching positioning model. Through adaptive particle fission and sampling, the problem of particle degradation and impoverishment in traditional particle filtering is solved, resulting in improved geomagnetic matching positioning accuracy. Finally, the proposed method was tested in a sea trial, and the results show that the proposed method has a lower positioning error than traditional particle-filtering and intelligent particle-filtering algorithms. Under geomagnetic map conditions, an average positioning accuracy of about 546.44 m is achieved.

show abstract

Section: Principle Of Geomagnetic Matching Localizationmentioning

confidence: 99%

Underwater Geomagnetic Localization Based on Adaptive Fission Particle-Matching Technology

Yu,

Li,

Yang

et al. 2023

JMSE

View full text Add to dashboard Cite

show abstract

“…Issartel uses SLAM technology for the underwater military to achieve underwater hiding for tasks [24]. Zhang utilizes BSLAM (Bathymetric Particle Filter SLAM) which is accurate and fast for oceanographic surveys, demining, and seabed mappings [25]. Yang proposes a SLAM localization algorithm using forward-looking sonar for deep-sea mining [26].…”

Section: Introductionmentioning

confidence: 99%

An Underwater Localization Method Based on Visual SLAM for the Near-Bottom Environment

Liu,

Wang,

et al. 2024

JMSE

View full text Add to dashboard Cite

The feature matching of the near-bottom visual SLAM is influenced by underwater raised sediments, resulting in tracking loss. In this paper, the novel visual SLAM system is proposed in the underwater raised sediments environment. The underwater images are firstly classified based on the color recognition method by adding the weights of pixel location to reduce the interference of similar colors on the seabed. The improved adaptive median filter method is proposed to filter the classified images by using the mean value of the filter window border as the discriminant condition to retain the original features of the image. The filtered images are finally processed by the tracking module to obtain the trajectory of underwater vehicles and the seafloor maps. The datasets of seamount areas captured in the western Pacific Ocean are processed by the improved visual SLAM system. The keyframes, mapping points, and feature point matching pairs extracted from the improved visual SLAM system are improved by 5.2%, 11.2%, and 4.5% compared with that of the ORB-SLAM3 system, respectively. The improved visual SLAM system has the advantage of robustness to dynamic disturbances, which is of practical application in underwater vehicles operated in near-bottom areas such as seamounts and nodules.

show abstract